Overview

Dataset statistics

Number of variables13
Number of observations6000
Missing cells0
Missing cells (%)0.0%
Duplicate rows19
Duplicate rows (%)0.3%
Total size in memory609.5 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Alerts

Dataset has 19 (0.3%) duplicate rowsDuplicates
B365H is highly correlated with B365A and 6 other fieldsHigh correlation
B365D is highly correlated with IWD and 2 other fieldsHigh correlation
B365A is highly correlated with B365H and 6 other fieldsHigh correlation
IWH is highly correlated with B365H and 6 other fieldsHigh correlation
IWD is highly correlated with B365D and 2 other fieldsHigh correlation
IWA is highly correlated with B365H and 6 other fieldsHigh correlation
LBH is highly correlated with B365H and 6 other fieldsHigh correlation
LBD is highly correlated with B365D and 2 other fieldsHigh correlation
LBA is highly correlated with B365H and 6 other fieldsHigh correlation
WHH is highly correlated with B365H and 6 other fieldsHigh correlation
WHD is highly correlated with B365D and 2 other fieldsHigh correlation
WHA is highly correlated with B365H and 6 other fieldsHigh correlation
B365H is highly correlated with B365A and 6 other fieldsHigh correlation
B365D is highly correlated with B365A and 6 other fieldsHigh correlation
B365A is highly correlated with B365H and 10 other fieldsHigh correlation
IWH is highly correlated with B365H and 6 other fieldsHigh correlation
IWD is highly correlated with B365D and 6 other fieldsHigh correlation
IWA is highly correlated with B365H and 10 other fieldsHigh correlation
LBH is highly correlated with B365H and 6 other fieldsHigh correlation
LBD is highly correlated with B365D and 6 other fieldsHigh correlation
LBA is highly correlated with B365H and 10 other fieldsHigh correlation
WHH is highly correlated with B365H and 6 other fieldsHigh correlation
WHD is highly correlated with B365D and 6 other fieldsHigh correlation
WHA is highly correlated with B365H and 10 other fieldsHigh correlation
B365H is highly correlated with B365A and 6 other fieldsHigh correlation
B365D is highly correlated with IWD and 2 other fieldsHigh correlation
B365A is highly correlated with B365H and 6 other fieldsHigh correlation
IWH is highly correlated with B365H and 6 other fieldsHigh correlation
IWD is highly correlated with B365D and 2 other fieldsHigh correlation
IWA is highly correlated with B365H and 6 other fieldsHigh correlation
LBH is highly correlated with B365H and 6 other fieldsHigh correlation
LBD is highly correlated with B365D and 2 other fieldsHigh correlation
LBA is highly correlated with B365H and 6 other fieldsHigh correlation
WHH is highly correlated with B365H and 6 other fieldsHigh correlation
WHD is highly correlated with B365D and 2 other fieldsHigh correlation
WHA is highly correlated with B365H and 6 other fieldsHigh correlation
B365H is highly correlated with IWH and 2 other fieldsHigh correlation
B365D is highly correlated with B365A and 7 other fieldsHigh correlation
B365A is highly correlated with B365D and 6 other fieldsHigh correlation
IWH is highly correlated with B365H and 3 other fieldsHigh correlation
IWD is highly correlated with B365D and 7 other fieldsHigh correlation
IWA is highly correlated with B365D and 6 other fieldsHigh correlation
LBH is highly correlated with B365H and 2 other fieldsHigh correlation
LBD is highly correlated with B365D and 6 other fieldsHigh correlation
LBA is highly correlated with B365D and 6 other fieldsHigh correlation
WHH is highly correlated with B365H and 3 other fieldsHigh correlation
WHD is highly correlated with B365D and 6 other fieldsHigh correlation
WHA is highly correlated with B365D and 6 other fieldsHigh correlation

Reproduction

Analysis started2021-12-27 01:00:22.703668
Analysis finished2021-12-27 01:00:45.753665
Duration23.05 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

FTR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.0 KiB
NH
3213 
H
2787 

Length

Max length2
Median length2
Mean length1.5355
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNH
2nd rowNH
3rd rowNH
4th rowH
5th rowH

Common Values

ValueCountFrequency (%)
NH3213
53.5%
H2787
46.5%

Length

2021-12-27T09:00:45.818667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-27T09:00:45.897665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nh3213
53.5%
h2787
46.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

B365H
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct144
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.701662333
Minimum1.08
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:45.988665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.08
5-th percentile1.25
Q11.67
median2.15
Q32.87
95-th percentile6.5
Maximum23
Range21.92
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.796118455
Coefficient of variation (CV)0.6648197418
Kurtosis11.51518168
Mean2.701662333
Median Absolute Deviation (MAD)0.54
Skewness2.794196935
Sum16209.974
Variance3.226041504
MonotonicityNot monotonic
2021-12-27T09:00:46.101665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1280
 
4.7%
2.5241
 
4.0%
2213
 
3.5%
2.2190
 
3.2%
2.25167
 
2.8%
2.3158
 
2.6%
1.8154
 
2.6%
1.83149
 
2.5%
2.4131
 
2.2%
1.25117
 
1.9%
Other values (134)4200
70.0%
ValueCountFrequency (%)
1.083
 
0.1%
1.091
 
< 0.1%
1.16
 
0.1%
1.118
 
0.1%
1.127
 
0.1%
1.138
 
0.1%
1.1429
0.5%
1.1432
 
< 0.1%
1.1617
0.3%
1.1672
 
< 0.1%
ValueCountFrequency (%)
231
 
< 0.1%
173
 
0.1%
154
 
0.1%
141
 
< 0.1%
138
 
0.1%
1211
 
0.2%
1115
0.2%
1019
0.3%
9.58
 
0.1%
936
0.6%

B365D
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.890617
Minimum2.5
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:46.227664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.2
Q13.25
median3.5
Q34
95-th percentile6
Maximum13
Range10.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation1.067555647
Coefficient of variation (CV)0.2743923771
Kurtosis11.74352662
Mean3.890617
Median Absolute Deviation (MAD)0.25
Skewness2.927512835
Sum23343.702
Variance1.139675059
MonotonicityNot monotonic
2021-12-27T09:00:46.362667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.25847
14.1%
3.4717
11.9%
3.3558
 
9.3%
3.2528
 
8.8%
3.5496
 
8.3%
3.6491
 
8.2%
4290
 
4.8%
3.75249
 
4.2%
4.5177
 
2.9%
5167
 
2.8%
Other values (40)1480
24.7%
ValueCountFrequency (%)
2.51
 
< 0.1%
2.8751
 
< 0.1%
330
 
0.5%
3.1131
 
2.2%
3.2528
8.8%
3.25847
14.1%
3.2920
 
0.3%
3.3558
9.3%
3.3927
 
0.4%
3.4717
11.9%
ValueCountFrequency (%)
134
 
0.1%
122
 
< 0.1%
118
 
0.1%
106
 
0.1%
9.53
 
0.1%
916
 
0.3%
8.515
 
0.2%
831
0.5%
7.529
0.5%
765
1.1%

B365A
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct133
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.828635333
Minimum1.16
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:46.498211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.16
5-th percentile1.55
Q12.5
median3.5
Q35.5
95-th percentile13
Maximum34
Range32.84
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.939662179
Coefficient of variation (CV)0.8158955703
Kurtosis6.950641847
Mean4.828635333
Median Absolute Deviation (MAD)1.25
Skewness2.366499065
Sum28971.812
Variance15.52093809
MonotonicityNot monotonic
2021-12-27T09:00:46.615497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5198
 
3.3%
3196
 
3.3%
4195
 
3.2%
3.4177
 
2.9%
5165
 
2.8%
3.75158
 
2.6%
3.5143
 
2.4%
5.5142
 
2.4%
2.5132
 
2.2%
6130
 
2.2%
Other values (123)4364
72.7%
ValueCountFrequency (%)
1.162
 
< 0.1%
1.181
 
< 0.1%
1.21
 
< 0.1%
1.224
 
0.1%
1.256
 
0.1%
1.287
 
0.1%
1.293
 
0.1%
1.315
0.2%
1.3321
0.4%
1.3631
0.5%
ValueCountFrequency (%)
343
 
0.1%
293
 
0.1%
271
 
< 0.1%
2613
 
0.2%
2314
 
0.2%
2118
 
0.3%
1945
0.8%
183
 
0.1%
1755
0.9%
167
 
0.1%

IWH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct122
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.507676667
Minimum1.05
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:46.744487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.25
Q11.65
median2.1
Q32.65
95-th percentile5.5
Maximum15
Range13.95
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.411207563
Coefficient of variation (CV)0.5627549923
Kurtosis7.648895308
Mean2.507676667
Median Absolute Deviation (MAD)0.5
Skewness2.377039201
Sum15046.06
Variance1.991506787
MonotonicityNot monotonic
2021-12-27T09:00:46.995487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2348
 
5.8%
2.1333
 
5.5%
2299
 
5.0%
2.3269
 
4.5%
1.9267
 
4.5%
2.4266
 
4.4%
1.8226
 
3.8%
2.5199
 
3.3%
1.75152
 
2.5%
1.85152
 
2.5%
Other values (112)3489
58.1%
ValueCountFrequency (%)
1.051
 
< 0.1%
1.081
 
< 0.1%
1.14
 
0.1%
1.1226
 
0.4%
1.1540
 
0.7%
1.1742
0.7%
1.182
 
< 0.1%
1.2102
1.7%
1.2263
1.1%
1.232
 
< 0.1%
ValueCountFrequency (%)
152
 
< 0.1%
118
0.1%
104
 
0.1%
9.61
 
< 0.1%
9.510
0.2%
96
0.1%
8.71
 
< 0.1%
8.511
0.2%
813
0.2%
7.81
 
< 0.1%

IWD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6449
Minimum2.5
Maximum10.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:47.122487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3
Q13.2
median3.3
Q33.8
95-th percentile5.5
Maximum10.5
Range8
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.8145256689
Coefficient of variation (CV)0.2234699632
Kurtosis9.546301073
Mean3.6449
Median Absolute Deviation (MAD)0.2
Skewness2.683333596
Sum21869.4
Variance0.6634520653
MonotonicityNot monotonic
2021-12-27T09:00:47.242487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2958
16.0%
3.3935
15.6%
3.1656
10.9%
3447
 
7.4%
3.5380
 
6.3%
3.4354
 
5.9%
3.6242
 
4.0%
4227
 
3.8%
3.7198
 
3.3%
3.9153
 
2.5%
Other values (52)1450
24.2%
ValueCountFrequency (%)
2.51
 
< 0.1%
2.61
 
< 0.1%
2.72
 
< 0.1%
2.82
 
< 0.1%
2.931
 
0.5%
2.951
 
< 0.1%
3447
7.4%
3.056
 
0.1%
3.1656
10.9%
3.1518
 
0.3%
ValueCountFrequency (%)
10.51
 
< 0.1%
101
 
< 0.1%
9.21
 
< 0.1%
9.11
 
< 0.1%
912
0.2%
86
 
0.1%
7.91
 
< 0.1%
7.517
0.3%
7.31
 
< 0.1%
717
0.3%

IWA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.185253333
Minimum1.2
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:47.380487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.55
Q12.5
median3.2
Q34.6
95-th percentile10.31
Maximum29
Range27.8
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.934304982
Coefficient of variation (CV)0.7011057033
Kurtosis6.978977883
Mean4.185253333
Median Absolute Deviation (MAD)1
Skewness2.28384784
Sum25111.52
Variance8.610145727
MonotonicityNot monotonic
2021-12-27T09:00:47.494488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8226
 
3.8%
3.2207
 
3.5%
2.7202
 
3.4%
3.3196
 
3.3%
2.9175
 
2.9%
3.6174
 
2.9%
3.1171
 
2.9%
2.6165
 
2.8%
2.5151
 
2.5%
4147
 
2.5%
Other values (125)4186
69.8%
ValueCountFrequency (%)
1.24
 
0.1%
1.221
 
< 0.1%
1.256
 
0.1%
1.274
 
0.1%
1.281
 
< 0.1%
1.319
 
0.3%
1.336
 
0.1%
1.3528
0.5%
1.374
 
0.1%
1.450
0.8%
ValueCountFrequency (%)
291
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
221
 
< 0.1%
2012
 
0.2%
1810
 
0.2%
171
 
< 0.1%
165
 
0.1%
1544
0.7%

LBH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct226
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.590269167
Minimum1.08
Maximum21.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:47.624487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.08
5-th percentile1.22
Q11.66
median2.1
Q32.75
95-th percentile6
Maximum21.06
Range19.98
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation1.610506916
Coefficient of variation (CV)0.6217527261
Kurtosis10.77229002
Mean2.590269167
Median Absolute Deviation (MAD)0.5
Skewness2.664595455
Sum15541.615
Variance2.593732525
MonotonicityNot monotonic
2021-12-27T09:00:47.744487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1277
 
4.6%
2.2272
 
4.5%
2214
 
3.6%
2.25197
 
3.3%
1.8195
 
3.2%
2.5184
 
3.1%
1.73145
 
2.4%
2.38145
 
2.4%
1.91144
 
2.4%
2.4126
 
2.1%
Other values (216)4101
68.3%
ValueCountFrequency (%)
1.083
 
0.1%
1.092
 
< 0.1%
1.17
 
0.1%
1.1110
0.2%
1.1212
0.2%
1.134
 
0.1%
1.1421
0.4%
1.157
 
0.1%
1.1612
0.2%
1.1673
 
0.1%
ValueCountFrequency (%)
21.061
 
< 0.1%
171
 
< 0.1%
151
 
< 0.1%
134
0.1%
12.461
 
< 0.1%
12.11
 
< 0.1%
127
0.1%
11.491
 
< 0.1%
11.091
 
< 0.1%
116
0.1%

LBD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct162
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.747798833
Minimum2.75
Maximum12.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:47.888079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.75
5-th percentile3.2
Q13.25
median3.4
Q33.75
95-th percentile5.75
Maximum12.59
Range9.84
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.9293678598
Coefficient of variation (CV)0.2479769863
Kurtosis14.24326278
Mean3.747798833
Median Absolute Deviation (MAD)0.2
Skewness3.1442089
Sum22486.793
Variance0.8637246188
MonotonicityNot monotonic
2021-12-27T09:00:48.006076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.21191
19.9%
3.4629
10.5%
3.25610
10.2%
3.3597
10.0%
3.5576
9.6%
3.75347
 
5.8%
3.6269
 
4.5%
4245
 
4.1%
4.5188
 
3.1%
5174
 
2.9%
Other values (152)1174
19.6%
ValueCountFrequency (%)
2.751
 
< 0.1%
2.81
 
< 0.1%
2.8751
 
< 0.1%
2.885
 
0.1%
2.910
 
0.2%
3100
1.7%
3.041
 
< 0.1%
3.051
 
< 0.1%
3.061
 
< 0.1%
3.081
 
< 0.1%
ValueCountFrequency (%)
12.591
 
< 0.1%
12.391
 
< 0.1%
121
 
< 0.1%
11.961
 
< 0.1%
113
0.1%
10.981
 
< 0.1%
10.91
 
< 0.1%
10.861
 
< 0.1%
10.681
 
< 0.1%
10.571
 
< 0.1%

LBA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct245
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.498023333
Minimum1.17
Maximum32.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:48.127076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.17
5-th percentile1.53
Q12.45
median3.3
Q35
95-th percentile12
Maximum32.7
Range31.53
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation3.472833699
Coefficient of variation (CV)0.7720799652
Kurtosis8.47182719
Mean4.498023333
Median Absolute Deviation (MAD)1.2
Skewness2.462405804
Sum26988.14
Variance12.0605739
MonotonicityNot monotonic
2021-12-27T09:00:48.241076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3279
 
4.7%
4.5239
 
4.0%
4209
 
3.5%
3.75192
 
3.2%
2.75190
 
3.2%
5187
 
3.1%
3.2178
 
3.0%
3.5165
 
2.8%
2.5162
 
2.7%
2.8159
 
2.6%
Other values (235)4040
67.3%
ValueCountFrequency (%)
1.171
 
< 0.1%
1.182
 
< 0.1%
1.22
 
< 0.1%
1.225
0.1%
1.255
0.1%
1.271
 
< 0.1%
1.285
0.1%
1.2910
0.2%
1.39
0.1%
1.311
 
< 0.1%
ValueCountFrequency (%)
32.72
 
< 0.1%
293
0.1%
28.681
 
< 0.1%
27.951
 
< 0.1%
27.281
 
< 0.1%
266
0.1%
24.791
 
< 0.1%
24.651
 
< 0.1%
23.721
 
< 0.1%
235
0.1%

WHH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct107
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.63024
Minimum1.06
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:48.370075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.06
5-th percentile1.22
Q11.66
median2.15
Q32.75
95-th percentile6
Maximum17
Range15.94
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation1.647163127
Coefficient of variation (CV)0.6262406195
Kurtosis8.588908896
Mean2.63024
Median Absolute Deviation (MAD)0.54
Skewness2.523792581
Sum15781.44
Variance2.713146367
MonotonicityNot monotonic
2021-12-27T09:00:48.482689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5228
 
3.8%
2.1201
 
3.4%
2.3195
 
3.2%
2.25171
 
2.9%
2167
 
2.8%
2.4166
 
2.8%
2.2163
 
2.7%
1.8149
 
2.5%
2.15146
 
2.4%
1.83140
 
2.3%
Other values (97)4274
71.2%
ValueCountFrequency (%)
1.062
 
< 0.1%
1.072
 
< 0.1%
1.082
 
< 0.1%
1.111
 
0.2%
1.116
 
0.1%
1.1213
 
0.2%
1.1435
0.6%
1.154
 
0.1%
1.1618
0.3%
1.1741
0.7%
ValueCountFrequency (%)
172
 
< 0.1%
152
 
< 0.1%
133
 
0.1%
125
 
0.1%
1114
0.2%
10.51
 
< 0.1%
1014
0.2%
9.510
0.2%
921
0.4%
8.514
0.2%

WHD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.649625
Minimum2.8
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:48.614710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3
Q13.2
median3.3
Q33.75
95-th percentile5.5
Maximum13
Range10.2
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.8885036199
Coefficient of variation (CV)0.2434506613
Kurtosis14.56153094
Mean3.649625
Median Absolute Deviation (MAD)0.2
Skewness3.133558689
Sum21897.75
Variance0.7894386825
MonotonicityNot monotonic
2021-12-27T09:00:48.878708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3.2990
16.5%
3.1980
16.3%
3.3754
12.6%
3.4438
 
7.3%
3.5339
 
5.7%
3.6276
 
4.6%
3.25275
 
4.6%
3271
 
4.5%
4223
 
3.7%
3.75207
 
3.5%
Other values (32)1247
20.8%
ValueCountFrequency (%)
2.87
 
0.1%
2.8715
 
0.2%
2.885
 
0.1%
2.943
 
0.7%
3271
 
4.5%
3.1980
16.3%
3.2990
16.5%
3.25275
 
4.6%
3.3754
12.6%
3.4438
7.3%
ValueCountFrequency (%)
131
 
< 0.1%
122
 
< 0.1%
113
 
0.1%
105
 
0.1%
9.51
 
< 0.1%
99
 
0.1%
8.54
 
0.1%
811
 
0.2%
7.517
0.3%
735
0.6%

WHA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.548745
Minimum1.14
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.0 KiB
2021-12-27T09:00:48.993711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile1.53
Q12.5
median3.3
Q35
95-th percentile12
Maximum29
Range27.86
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.556692858
Coefficient of variation (CV)0.7819064067
Kurtosis7.004574426
Mean4.548745
Median Absolute Deviation (MAD)1.15
Skewness2.36912121
Sum27292.47
Variance12.65006409
MonotonicityNot monotonic
2021-12-27T09:00:49.107709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4200
 
3.3%
4.5195
 
3.2%
5194
 
3.2%
3179
 
3.0%
8174
 
2.9%
3.2169
 
2.8%
3.5165
 
2.8%
6161
 
2.7%
3.1159
 
2.6%
5.5159
 
2.6%
Other values (92)4245
70.8%
ValueCountFrequency (%)
1.141
 
< 0.1%
1.151
 
< 0.1%
1.161
 
< 0.1%
1.22
 
< 0.1%
1.222
 
< 0.1%
1.2512
0.2%
1.271
 
< 0.1%
1.285
0.1%
1.294
 
0.1%
1.39
0.1%
ValueCountFrequency (%)
293
 
0.1%
269
 
0.1%
234
 
0.1%
2128
 
0.5%
1922
 
0.4%
1737
 
0.6%
15103
1.7%
1355
0.9%
1294
1.6%
11103
1.7%

Interactions

2021-12-27T09:00:43.510077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:25.549861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.049978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.730027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.396544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.135546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.783107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:35.369658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.179745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.655742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.399370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.043287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.632078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:25.674895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.167981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.857542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.521546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.263546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.903106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:35.506660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.292746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.777743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.526370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.155288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.761080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:25.795892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.428009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.989541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.653541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.398547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.030107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:35.781685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.411744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.907744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.659369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.276286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.900351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:25.926917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.562011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.134543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.795058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.543079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.170108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:35.926685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.540743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.048742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.803369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.405564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.181350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.049923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.691010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.274544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.928056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.681079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.305106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.067685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.664742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.189256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.940402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.528564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.320351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.182919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.825037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.423546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:31.067057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.826080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.444144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.212688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.797742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.328256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.083401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.658564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.453534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.308920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:27.954037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.564543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:31.201057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.963107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.576142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.351686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.923744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.460257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.221403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.781563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.591049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.444921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.090039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.711541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:31.342057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.111107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.716141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.497685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.055742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.601256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.366403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:42.912564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.711050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.557917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.213039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.840543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:31.602343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.241108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.839144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.625227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.168742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.724256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.494400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.023566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.844050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.679923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.345029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:29.981544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:31.735346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.376107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:34.972145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.766229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.291742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:39.999370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.634290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.148566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:44.981683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.808917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.481026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.129542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:31.877344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.519107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:35.111142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:36.911228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.420744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.140373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.777287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.275563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:45.103683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:26.922978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:28.599025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:30.257544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:32.000345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:33.647106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:35.233660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:37.039743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:38.531742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:40.264370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:41.906288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-27T09:00:43.388078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-12-27T09:00:49.225708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-27T09:00:49.456725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-27T09:00:49.677725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-27T09:00:49.902753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-27T09:00:45.311712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-27T09:00:45.622742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FTRB365HB365DB365AIWHIWDIWALBHLBDLBAWHHWHDWHA
0NH1.7273.254.3331.803.13.81.6153.255.001.663.34.50
1NH2.8003.252.2002.903.02.22.8003.202.202.753.12.30
2NH2.2503.252.7502.303.02.72.2503.202.752.303.12.75
3H1.7273.254.3331.803.13.81.8333.203.751.723.24.33
4H1.6673.404.5001.703.24.21.6153.504.501.663.34.50
5H1.2005.0012.0001.205.010.01.2005.0011.001.205.011.00
6H1.2225.0010.0001.254.59.01.2504.5010.001.225.09.50
7NH3.0003.252.1002.903.02.23.0003.202.103.203.02.10
8H1.5713.405.5001.653.34.41.6153.504.501.573.55.00
9NH2.7503.252.2502.503.02.52.7503.202.252.703.02.37

Last rows

FTRB365HB365DB365AIWHIWDIWALBHLBDLBAWHHWHDWHA
5990H2.623.23.002.603.102.952.723.152.992.623.102.88
5991NH2.903.62.502.853.552.403.033.592.442.903.502.38
5992NH2.603.13.102.503.202.952.733.053.082.623.003.00
5993H1.227.513.001.226.5012.501.227.0113.261.207.0013.00
5994H1.188.019.001.207.0013.001.187.7418.361.158.0015.00
5995H1.643.96.001.703.805.001.633.996.371.603.906.00
5996H1.953.64.332.003.403.802.023.484.201.953.504.00
5997NH7.504.51.507.204.401.457.644.371.517.004.331.47
5998H1.574.56.001.654.005.101.534.516.761.524.336.00
5999H4.103.91.903.753.801.903.873.901.983.903.751.91

Duplicate rows

Most frequently occurring

FTRB365HB365DB365AIWHIWDIWALBHLBDLBAWHHWHDWHA# duplicates
0H1.206.5015.001.205.2011.01.206.0015.001.206.015.02
1H1.207.5017.001.225.5012.01.226.0013.001.225.515.02
2H1.295.5011.001.304.808.51.295.5010.001.295.510.02
3H1.404.508.501.354.507.31.404.508.001.444.08.02
4H1.853.604.751.903.453.81.903.504.331.913.14.52
5H1.903.104.331.903.103.51.833.203.751.903.23.52
6H2.403.253.002.503.202.62.383.253.002.403.23.02
7NH1.444.007.501.453.805.41.443.606.501.503.46.02
8NH1.444.208.001.404.307.51.404.508.001.444.08.02
9NH1.833.504.501.853.403.81.803.504.501.833.44.52